Search NASA⌕ Search

SEARCH · Search NASA

Results for “Harvest 5”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
NASA NTRS1997

Selection and Characterization of Vegetable Crop Cultivars for use in Advanced Life Support Systems

Cultivar evaluation for controlled environments is a lengthy and multifaceted activity. The chapters of this thesis cover eight steps preparatory to yield trials, and the final step of cultivar selection after data are collected. The steps are as follows: 1. Examination of the literature on the crop and crop cultivars to assess the state of knowledge. 2. Selection of standard cultivars with which to explore crop response to major growth factors and determine set points for screening and, later, production. 3. Determination of practical growing techniques for the crop in controlled environments. 4. Design of experiments for determination of crop responses to the major growth factors, with particular emphasis on photoperiod, daily light integral and air temperature. 5. Developing a way of measuring yield appropriate to the crop type by sampling through the harvest period and calculating a productivity function. 6. Narrowing down the pool of cultivars and breeding lines according to a set of criteria and breeding history. 7. Determination of environmental set points for cultivar evaluation through calculating production cost as a function of set points and size of target facility. 8. Design of screening and yield trial experiments emphasizing efficient use of space. 9. Final evaluation of cultivars after data collection, in terms of production cost and value to the consumer. For each of the steps, relevant issues are addressed. In selecting standards to determine set points for screening, set points that optimize cost of production for the standards may not be applicable to all cultivars. Production of uniform and equivalent- sized seedlings is considered as a means of countering possible differences in seed vigor. Issues of spacing and re-spacing are also discussed.

Langhans, Robert W.↗
DOE OSTI2020

Radiolysis and radionuclide production in a flowing-water target during fast 40 Ca 20+ irradiation

Here, a flowing-water target was irradiated with a 140 MeV/u, 8 nA 40Ca20+ beam to test the feasibility of isotope harvesting at the upcoming Facility for Rare Isotope Beams. Among other radionuclides, 2.6(2)E-6 48Cr and 5.6(5)E-6 28 Mg nuclei were formed for every impingent 40Ca and were collected through ion exchange. Radiolysis-induced molecular hydrogen evolved from the target at an initial rate of 0.91(9) H2 molecules per 100 eV of beam energy deposited. No radiation-accelerated corrosion of the target material was determined.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2024

Writing and Detecting Topological Charges in Exfoliated Fe 5– x GeTe 2

Fe 5–x GeTe 2 is a promising two-dimensional (2D) van der Waals (vdW) magnet for practical applications, given its magnetic properties. These include Curie temperatures above room temperature, and topological spin textures–TST (both merons and skyrmions), responsible for a pronounced anomalous Hall effect (AHE) and its topological counterpart (THE), which can be harvested for spintronics. Here, we show that both the AHE and THE can be amplified considerably by just adjusting the thickness of exfoliated Fe 5–x GeTe 2 , with THE becoming observable even in zero magnetic field due to a field-induced unbalance in topological charges. Using a complementary suite of techniques, including electronic transport, Lorentz transmission electron microscopy, and micromagnetic simulations, we reveal the emergence of substantial coercive fields upon exfoliation, which are absent in the bulk, implying thickness-dependent magnetic interactions that affect the TST. We detected a “magic” thickness t ≈ 30 nm where the formation of TST is maximized, inducing large magnitudes for the topological charge density (~6.45 × 10 20 cm –2 ), and the concomitant anomalous (ρ xy A,max ≃22.6 μΩ cm) and topological (ρ xy u,T 1≃5 μΩ cm) Hall resistivities at T ≈ 120 K. These values for ρ xy A,max and ρ xy u,T are higher than those found in magnetic topological insulators and, so far, the largest reported for 2D magnets. Furthermore, the hitherto unobserved THE under zero magnetic field could provide a platform for the writing and electrical detection of TST aiming at energy-efficient devices based on vdW ferromagnets.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2026

Optimizing bioenergy biofuel harvest: a comparative analysis of stepwise and integrated methods for economic and environmental sustainability

Switchgrass is a promising bioenergy feedstock due to its high biomass yield potential, adaptability to marginal lands, and low carbon intensity for feedstock production. However, accurate cost estimation and assessment of greenhouse gas (GHG) emissions for the energy-intensive harvesting process are essential for evaluating the sustainability of bioenergy. This study provides a comparative analysis of two harvesting methods: the Stepwise Method, which separates operations into multiple stages, and the Integrated Method, which combines mowing and raking into a single pass. The analysis was conducted under four scenarios based on field sizes and biomass yields. Using three years of field-scale switchgrass harvest data from 125 sites, GHG emissions, energy consumption, and harvesting costs were quantified using the GREET model and techno-economic analysis. Additionally, regression analysis identified key climate and operational factors affecting fuel consumption. The Stepwise method was the most cost-effective for large fields with high biomass yield, achieving the lowest harvesting costs ($37.70 per ton). In contrast, the Integrated Method performed better in small fields and low-yield conditions, reducing GHG emissions by 9 % and energy use by 5 %. Regression analysis confirmed that a larger field size reduced fuel consumption, while higher biomass yield and longer operational time increased fuel use. Maximum temperature also contributed to a slight increase in fuel consumption. Furthermore, these results provide actionable insights for optimizing harvesting strategies based on field-specific conditions and operational goals, contributing to the economic and environmental sustainability of bioenergy production.

60 APPLIED LIFE SCIENCES↗
NASA NTRS2016

Simulated Space Radiation: Murine Skeletal Responses During Recovery and with Mechanical Stimulation

Simulated space radiation at doses similar to those of solar particle events or a round-trip sojourn to Mars (1-2Gy) may cause skeletal tissue degradation and deplete stem/progenitor cell pools throughout the body. We hypothesized that simulated space radiation (SSR) causes late, time-dependent deficits in bone structure and bone cell function reflected by changes in gene expression in response to anabolic stimuli. We used a unique sequential dual ion exposure (proton and iron) for SSR to investigate time-dependence of responses in gene expression, cell function, and microarchitecture with respect to radiation and an anabolic stimulus of axial loading (AL). Male 16-wk C57BL6/J mice (n=120 total) were exposed to 0Gy (Sham, n=10), 56Fe (2Gy, positive control dose, n=10), or sequential ions for SSR (1Gy 1H/56Fe/1H, n=10) by total body irradiation (IR), and the tissues were harvested 2 or 6 mo. later. Further, to assess the response to anabolic stimuli, we subjected additional Sham-AL (n=15) and SSR-AL (n=15) groups to rest-inserted tibial axial loading (AL) starting at 1 and 5 months post-IR (-9N, 60 cycles/day, 3 days/wk, 4 wks). Exposure to 56Fe caused a significant reduction in cancellous bone volume fraction (BV/TV) compared to Sham (-34%) and SSR (-20%) in the proximal tibia metaphysis at 2-months post-IR; however BV/TV for SSR group was not different than Sham. Both 56Fe and SSR caused significant reduction in trabecular number (Tb.N) compared to Sham (-33% and -16%, respectively). Further, Tb.N for 56Fe (2Gy) was significantly lower than SSR (-21%). Ex vivo culture of marrow cells to assess growth and differentiation of osteoblast lineage cells 6 months post-IR showed that both 56Fe and SSR exposures significantly impaired colony formation compared to Sham (-66% and -54%, respectively), as well as nodule mineralization (-90% and -51%, respectively). Two-way analysis of variance showed that both mechanical loading and radiation reduced BV/TV, mechanical loading reduced trabecular thickness (Tb.Th), and radiation reduced Tb.N, at both time points. To assess acute response to mechanical stimuli, samples were harvested from a subset of Sham-AL (n=5) and SSR-AL (n=5) to measure changes in gene expression levels. Preliminary results indicate that axial loading increased expression of the antioxidant response gene Nfe2l2 and the osteoprogenitor-associated marker Runx2 in the bone marrow cells, and there was an interaction effect between axial loading and radiation at 2-months post-IR. Additional analyses of gene expression levels in the mineralized tissue are in progress. Results indicate that SSR caused persistent impairment of osteoblast colony formation and nodule mineralization 6-mo post-IR. Contrary to our hypothesis, simulated space radiation did not impair the ability of cancellous bone to respond to a mechanical anabolic stimulus, consistent with our previous findings [1]. Hence, compressive loading may be a potential countermeasure against spaceflight-induced bone loss.

Bone Loss↗
NASA NTRS2016

Simulated Space Radiation: Murine Skeletal Responses During Recovery and with Mechanical Stimulation

Simulated space radiation at doses similar to those of solar particle events or a round-trip sojourn to Mars (1-2Gy) may cause skeletal tissue degradation and deplete stem/progenitor cell pools throughout the body. We hypothesized that simulated space radiation (SSR) causes late, time-dependent deficits in bone structure and bone cell function reflected by changes in gene expression in response to anabolic stimuli. We used a unique sequential dual ion exposure (proton and iron) for SSR to investigate time-dependence of responses in gene expression, cell function, and microarchitecture with respect to radiation and an anabolic stimulus of axial loading (AL). Male 16-wk C57BL6/J mice (n=120 total) were exposed to 0Gy (Sham, n=10), 56Fe (2Gy, positive control dose, n=10), or sequential ions for SSR (1Gy 1H/56Fe/1H, n=10) by total body irradiation (IR), and the tissues were harvested 2 or 6 mo. later. Further, to assess the response to anabolic stimuli, we subjected additional Sham-AL (n=15) and SSR-AL (n=15) groups to rest-inserted tibial axial loading (AL) starting at 1 and 5 months post-IR (-9N, 60 cycles/day, 3 days/wk, 4 wks). Exposure to 56Fe caused a significant reduction in cancellous bone volume fraction (BV/TV) compared to Sham (-34%) and SSR (-20%) in the proximal tibia metaphysis at 2-months post-IR; however BV/TV for SSR group was not different than Sham. Both 56Fe and SSR caused significant reduction in trabecular number (Tb.N) compared to Sham (-33% and -16%, respectively). Further, Tb.N for 56Fe (2Gy) was significantly lower than SSR (-21%). Ex vivo culture of marrow cells to assess growth and differentiation of osteoblast lineage cells 6 months post-IR showed that both 56Fe and SSR exposures significantly impaired colony formation compared to Sham (-66% and -54%, respectively), as well as nodule mineralization (-90% and -51%, respectively). Two-way analysis of variance showed that both mechanical loading and radiation reduced BV/TV, mechanical loading reduced trabecular thickness (Tb.Th), and radiation reduced Tb.N, at both time points. To assess acute response to mechanical stimuli, samples were harvested from a subset of Sham-AL (n=5) and SSR-AL (n=5) to measure changes in gene expression levels. Preliminary results indicate that axial loading increased expression of the antioxidant response gene Nfe2l2 and the osteoprogenitor-associated marker Runx2 in the bone marrow cells, and there was an interaction effect between axial loading and radiation at 2-months post-IR. Additional analyses of gene expression levels in the mineralized tissue are in progress. Results indicate that SSR caused persistent impairment of osteoblast colony formation and nodule mineralization 6-mo post-IR. Contrary to our hypothesis, simulated space radiation did not impair the ability of cancellous bone to respond to a mechanical anabolic stimulus, consistent with our previous findings. Hence, compressive loading may be a potential countermeasure against spaceflight-induced bone loss.

MURINE SKELETAL↗
DOE OSTI2023

FUN-BioCROP model with litter decomposition parameters derived from the LIDET dataset

This repository contains the code and data necessary to run the FUN-BioCROP (Fixation and Uptake of Nitrogen-Bioenergy Carbon, Rhizosphere, Organisms, and Protection) model with litter decomposition parameters derived from a modified Monte Carlo simulation that used the Long-term Intersite Decomposition Experiment Team dataset. Related publication:Juice, S.M., Ridgeway, J.R., Hartman, M.D., Parton, W.J., Berardi, D.M., Sulman, B.N., Allen, K.E., & Brzostek, E.R. Reparameterizing litter decomposition using a simplified Monte Carlo method improves litter decay simulated by a microbial model and alters bioenergy soil carbon estimates. Description of Files: FUNBioCROP_LIDET Study.Rmd R code with FUN-BioCROP model that can be run with 10 different sets of parameters for litter decomposition (Baseline, LIDET, or eight other best parameter sets identified in the modified Monte Carlo simulation. CORPSE Functions_Bioenergy_V2.R Code with CORPSE model functions, called by FUNBioCROP_LIDET Study.Rmd Model Input Data: bulk.csv, bulk_till.csv, rhizo.csv, rhizo_till.csv, litter.csv Initial C and N (kg C or N/m2) pool values for each soil compartment, final values from spin up. All five files have the same columns: (Column - Description - Units) uFastC - Unprotected fast decomposing carbon - kg carbon/m2 uSlowC - Unprotected slow decomposing carbon - kg carbon/m2 uNecroC - Unprotected necromass carbon - kg carbon/m2 pFastC - Protected fast decomposing carbon - kg carbon/m2 pSlowC - Protected slow decomposing carbon - kg carbon/m2 pNecroC - Protected necromass carbon - kg carbon/m2 livingMicrobeC - Carbon in living microbial biomass - kg carbon/m2 uFastN - Unprotected fast decomposing nitrogen - kg nitrogen/m2 uSlowN - Unprotected slow decomposing nitrogen - kg nitrogen/m2 uNecroN - Unprotected necromass nitrogen - kg nitrogen/m2 pFastN - Protected fast decomposing nitrogen - kg nitrogen/m2 pSlowN - Protected slow decomposing nitrogen - kg nitrogen/m2 pNecroN - Protected necromass nitrogen - kg nitrogen/m2 inorganicN - Inorganic nitrogen - kg nitrogen/m2 CO2 - Carbon in carbon dioxide - kg carbon/m2 livingMicrobeN - Nitrogen in living microbial biomass - kg nitrogen/m2 Model Input Data: FluxTower_AvgSoilT.csv: Average daily soil temperature (oC) at 10 cm depth at University of Illinois Urbana-Champaign (UIUC) Energy Farm flux tower from 7/2008-3/2016. (One year of averaged data) Model Input Data: FluxTower_AvgSoilVWC.csv: Average daily soil volumetric water content (VWC) at 10 cm depth at UIUC Energy Farm flux tower from 7/2008-3/2016. (One year of averaged data) Model Input Data: input_CCS_LIDET Study.csv: This file has daily data to run FUN-BioCROP (Column - Description - Units): yr - calendar year - year doy - day of year (1 to 365) (no leap year) - day anpp - aboveground NPP (DayCent) - kg C/m2/day bnpp - belowground NPP (DayCent) - kg C/m2/day aglivc - live aboveground biomass carbon (DayCent) - kg C/m2 bglivcj - live juvenile fine root biomass carbon (DayCent) - kg C/m2 bglivcm - live mature fine root biomass carbon (DayCent) - kg C/m2 aglivn - live aboveground biomass nitrogen (DayCent) - kg N/m2 bglivnj - live juvenile fine root biomass nitrogen (DayCent) - kg N/m2 bglivnm - live mature fine root biomass nitrogen (DayCent) - kg N/m2 nyr - simulation year - year cult - indicates a cultivation event (0 or 1) crop - indicates a new crop (0 or 1) fert - indicates a fertilizer event (0 or 1) frst - indicates the first day of the growing season (0 or 1) harv - indicates a harvest event (0 or 1) last - indicates the end of the growing season (0 or 1) croptype - crop type (0=none; 1=alfalfa; 2=corn; 3=grass clover pasture; 4=soybean; 5=wheat) cropsrl - crop specific root length - mm/g root cultrhizmix - fraction of rhizosphere mixed with bulk soil during cultivation (0.0-1.0) - fraction cultlitmix - fraction of litter mixed with bulk soil during cultivation (0.0-1.0) - fraction harvremov - fraction of above ground biomass removed during harvest (0.0-1.0) - fraction fertamt - fertilization amount - g N/m2 lifehist - plant life history (0 = annual, 1 = perennial) froot_turnover_c - amount of C in fine root turnover - kg C/m2 froot_turnover_n - amount of N in fine root turnover - kg N/m2 agrd_turnover_c - amount of C in aboveground biomass turnover - kg C/m2 agrd_turnover_n - amount of N in aboveground biomass turnover - kg N/m2 leaf_litter_fastfrac - Fast decomposing fraction of leaf litter (0.0-1.0) - fraction root_litter_fastfrac - Fast decomposing fraction of root litter (0.0-1.0) - fraction root_diameter - root diameter - mm root_length - root length - mm root/m2 rhizo_frac - fraction of total soil volume that is rhizosphere (0.0 - 1.0) - fraction date - date in format YYYY-MM-DD Instructions: Save the model code ("FUN-BioCROP_LIDET Study.Rmd") and accompanything files (data streams and CORPSE function code) in the same folder. In model code "Chunk 3: Load CORPSE Data Streams" set the working directory (setwd) to the folder with the files saved in step #1. In "Chunk 5: Define LIDET parameter sets" select the litter decomposition parameter set to be used in the run, and comment out all other sets. If changing any parameter values, edit them in "Chunk 6: Load parameters." Run all chunks up to and including "Chunk 10: Prepare Data for Export." In "Chunk 11: Export Output Data" edit data frames for export and filenames, as necessary. "Chunk 12: Graph Total Soil C" makes a figure of C remaining over the model run period. Description of each model chunk (in file FUN-BioCROP_LIDET Study.Rmd): Chunk 1: Remove all functions, clear memory. Removes all functions from R environment, clears the memory. Chunk 2: Load Packages. Loads packages necessary to run the code. Chunk 3: Load CORPSE Data Streams. Sets the working directory and loads the data files necessary to run CORPSE. Chunk 4: Load CORPSE Functions. Loads the R script with CORPSE functions from the working directory, "CORPSE Functions_Bioenergy_V2.R". Chunk 5: Define LIDET parameter sets. Has ten different parameter sets for litter decomposition tested in this study: Baseline parameters, LIDET parameters, and the other 8 best performing parameter sets identified in the modified Monte Carlo. To run the model, all but one parameter set must be commented out. Chunk 6: Load Parameters. Loads all fixed parameters to run the model. Data frame with definitions of parameters is in the CORPSE function script "CORPSE Functions_Bioenergy_V2.R" Chunk 7: Prepare Data Streams. Takes data streams loaded in Chunk 3 and puts them in the format necessary to run the model. The model is coded to run at least two sites at a time, so if only one site is being run it must be run in duplicate. Individual data tables of daily values are created in this chunk from the input data file. Chunk 8: Set Initial Conditions. Creates data tables of soil C and N pools for each soil compartment (rhizo_till, rhizo, bulk_till, bulk, litter) and loads initial values into the data tables. Creates lists for each soil compartment to hold model output. Chunk 9: Load FUN Data and Set Up Matrices. Uses DayCent data to calculate FUN input data: root and leaf N demand, total N demand, plant CN, leaf N available for retranslocation, and litter production. Creates matrices for FUN model outputs. Chunk 10: Run Model. Runs the model. Chunk 11: Prepare Data for Export. Combines data from each day saved as lists into data frames for each soil compartment. Adds values from all soil compartments together to calculate total soil values, creates separate data frames for each soil C and N pool (e.g., protected slow C) for the total soil value. Adds different C and N pools together to calculate total soil C and N for all layers. Creates data frame of ratio of protected to unprotected SOC. Organizes FUN data for export. Chunk 12: Export Results. Exports CSV files of model results to the working directory. Chunk 13: Graph Total Soil C. Makes figure of C remaining over time. Related Links: Original FUN-BioCROP model: https://github.com/BrzostekEcologyLab/FUN-BioCROP LIDET dataset: https://andlter.forestry.oregonstate.edu/data/abstract.aspx?dbcode=TD023

Juice, Stephanie↗
DOE OSTI2021

Response to “how robust are reductions in modeled estimates from GTAP-BIO of the indirect land use change induced by conventional biofuels?”

Malins et al. (2020) recently published "How robust are reductions in modeled estimates from GTAP-BIO of the indirect land use change induced by conventional biofuels?", provided their narrative from the model improvements in GTAP-BIO over time, made several critical points regarding this model, and argued that the implemented improvements in this model tended to decrease Induce Land Use Changes (ILUC) emissions. Furthermore, they also provided several critical points regarding the Carbon Calculator for Land Use Change from Biofuels Production (CCLUB) emissions model. In this response to Malins et al. we address these critiques point-by-point by providing our detailed responses to key issues: 1) the GTAP-BIO model and its improvements over time; 2) the inclusion of cropland pasture in the model; 3) the observed land use changes in the US that have been the bases for improvements in GTAP-BIO model, 4) the time trends in corn price and yield to challenge the concept of the yield to price response; 5) some sources of land intensification in crop production; 6) the FAO notifications with respect to the comparison between harvested area and arable land; and 7) the GTAP-BIO results for multiple cropping. We also provided responses to Malins et al. critical points regarding the CCLUB emissions model. We hope that this response letter will open more constructive discussion among the LUC modeling community to remain focused on the big picture regarding agriculture's role as a very effective GHG mitigation tool that can shape the new policies to govern the production and consumption of biofuels.

09 BIOMASS FUELS↗
NASA NTRS1985

Remote sensing of coastal wetlands biomass using Thematic Mapper wavebands

Spectral data, simulating thematic mapper bands 3, 4 and 5 are gathered in salt and brackish marshes using a hand-held radiometer. Simple regression models are developed equating spectral radiance indices with total live biomass for S. alterniflora in a salt marsh and for a variety of plant species in a brackish marsh. Models are then tested using an independent set of data and compared to harvest estimates of biomass. In the salt marsh, biomass estimates from spectral data are similar to harvest biomass estimates during most of the growing season. Estimates of annual net aerial primary productivity calculated from spectral data are within 21% of production estimated from harvest data. During August, biomass estimates from spectral data in the brackish marsh are similar to biomass estimated by harvesting techniques. At other times during the growing season, spectral data estimates of biomass are not always comparable to harvest biomass estimates. Reasonable estimates of wetlands biomass are possible during the peak of the growing season (August) using spectral data similar to thematic mapper bands 3, 4 and 5 gathered with hand-held radiometers.

Hardisky, M. A.↗
NASA NTRS1974

Estimate of winter wheat yield from ERTS-1

A model for estimating wheat yield per acre has been applied to acreage estimates derived from ERTS-1 imagery to project the 1973 wheat yields for a ten county area in southwest Kansas. The results (41.04 million bushels) are within 3 per cent of the preharvest estimates for the same area prepared by the USDA Statistical Reporting Service (39.91 million bushels). The projection from ERTS data is based on a visual enumeration of all detectable wheat fields in the study area and was completed while the harvest was in progress. Visual identification of winter wheat is readily achieved by using a temporal sequence of images (band 5 for Sept.-Oct.; band 5 for Dec.-Jan.; and band 5 and 7 for March-April). Identification can be improved by stratifying the project area into subregions having more or less homogeneous agricultural practices and crop mixes. By doing this, small changes in the spectral appearance of wheat related to soil type, irrigation, etc. can be accounted for. The interpretation rules developed by visual analysis can be automated for rapid computer surveys.

Morain, S. A.↗
NASA NTRS2021

The Arya Crop Yield Forecasting Algorithm: Application to the Main Wheat Exporting Countries

Wheat is the most important commodity traded in the international food market. Thus, accurate and timely information on wheat production can help mitigate food price fluctuations. Within the existing operational regional and global scale agricultural monitoring systems that provide information on global crop yield and area forecasts, there are still fundamental gaps: #1. Lack of quantitative Earth Observation (EO) derived crop information, #2. Lack of global but detailed (national or subnational level) and timely crop production forecasts and #3. Lack of information on forecast uncertainties. In this study we present the Agriculture Remotely-sensed Yield Algorithm (ARYA) an EO-based method, advancing the state of EO data application and usage (addressing gap #1) to forecast wheat yield. The algorithm is based on the evolution of the Difference Vegetation Index (DVI) using MODIS data at 1km resolution and the Growing Degree Days (GDD) from reanalysis data. Additionally, we explore how Land Surface Temperature (LST) can be included into the model and whether this parameter adds any value to the model performance when combined with the optical information. ARYA is implemented at the national and subnational level to forecast winter wheat yield in the main wheat exporting countries of US, Russia, Ukraine, France, Germany, Australia and Argentina from 2001 to 2019 (covering over 70% of wheat exports globally) in a timely manner by providing daily forecasts (addressing gap #2). The results show that ARYA provides yield estimations with RMSE’s within 0.3 ± 0.1 t/ha at national level and 0.6 ± 0,1 t/ha at subnational level after Day Of the Year (DOY) 140 (mid May) in the Northern Hemisphere and DOY 280 (beginning of October) in the Southern Hemisphere. This means that ARYA can provide crop yield estimates of wheat yield with 5-15 % error at national and 7-20 % error at subnational level starting from 2 to 2.5 months prior to harvest.

Agriculture↗
DOE OSTI2020

Herbicide, fertilization, and planting density effects on intensively managed loblolly pine early stand development

Production forestry in the southeast US has been partially transitioned to intensively managed short rotations (~10 years), in which multiple silvicultural interventions are performed during forest development. Understanding the responses to silvicultural practices and continued refinement of site-specific recommendations is critical to sustainably maximize forest production. We evaluated the effects of silvicultural practices (herbicide, fertilization, and planting density) on growth, stand homogeneity, and above- and belowground biomass accumulation and partitioning of loblolly pine (Pinus taeda) throughout early stand development (age 5 years) in the southeastern US. Five treatments with eight replications each were tested: no herbicide and no fertilization (C); herbicide only (H); herbicide and half-reduced fertilization rate (R); herbicide and full fertilization (F); and increased stand density (60+ %; 1346 vs. 2152 trees per hectare) with herbicide and full fertilization rate (D). Allometric equations generated from destructive harvests were applied to annual diameter measurements to estimate plot-level biomass and allocation. Herbicide was crucial to promote stand uniformity and increase yield (~600+ % stem biomass compared with C at age 5). Aboveground biomass was similar in R and F treatments, which was ~25% higher than in H at age 5. Increasing planting density along with multiple herbicide and fertilizer applications yielded higher biomass without compromising individual tree size (diameter and height). There was little effect of silviculture practices on allocation patterns. Our results parallel what was found for fertilization with herbicide from a number of loblolly stands under similar conditions and indicate a ~28% volume gain with fertilization during early stand development. Similarly, our results were consistent with other studies implementing similar differences in planting density and suggest a ~26% volume gain through early stand development with an initial 60% increase in planting density. Furthermore, our study helps to understand complex relationships between production and silvicultural practices during early stand development and demonstrates that silvicultural prescriptions can be optimized to increase sustainability of production forestry

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Triplet photodynamic and up-conversion luminescence in donor–acceptor dyads with slip-stacked vs. co-facial arrangement

We report the design/synthesis and characterization of organic donor–acceptor (D–A) dyads can provide precious data allowing to improve the efficiency of classical photo-induced bimolecular interactions/processes. In this report, two novel triplet D–A dyads (4 and 5) were synthesized and fully characterized. While the optical absorption and emission profiles of these new systems exhibit similar spectral structures as that of the triplet donor/sensitizer quinoidal thioamide (QDN), the transient absorption (TA) spectra of these two dyads produced new features that can be associated with triplet transients and charge transfer species. However, the kinetics of the excited-state processes/dynamics is significantly influenced by the geometrical arrangement(s) of donor/acceptor chromophores. Further analysis of the TA data suggests that the dyad with slip-stack geometry (4) is less effective in undergoing both intra- and inter-dyad triplet energy transfer than the dyad with co-facial geometry (5). Subsequently, triplet sensitization of 9,10-diphenylanthracene (DPA) using both dyads led to upconverted photoluminescence via triplet–triplet annihilation of DPA triplet transients. But, it was found that a maximum upconversion quantum yield could be achieved at a low power density using the co-facial type dyad 5. Altogether, these results provide valuable guidance in the design of triplet donor–acceptor dyads, which could be used for light-harvesting/modulation applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Greenhouse Gas Production and Soil Chemistry in Anaerobic Soil Microcosm Incubations after Nitrogen Addition, Teller Road Site, Seward Peninsula, 2018-2019

This dataset provides the results of laboratory soil microcosm incubation experiments conducted under anaerobic conditions with and without the addition of ammonium chloride (NH4Cl). The microcosms contained soil collected in April 2017 from the NGEE Arctic Teller Road Site at mile marker 27 (TL_MM27) near Intensive Site 9 (Toeslope) and Intensive Site 5 (Plateau). Incubations were conducted at 8°C for 55 days, with repeated measurements of headspace greenhouse gas concentrations. Microcosms were destructively harvested and extracted using ultrapure water and 0.1 M KCl for analysis of the porewater geochemistry. This dataset contains two csv files, a pdf user guide. and the raw data in a zip file.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

In situ inelastic neutron scattering of mixed CH 4 –CO 2 hydrates

An abundant source of CH 4 can be found in natural hydrate deposits. Recent demonstration of CH 4 recovery from hydrates via CO 2 exchange has revealed the potential as a fuel source that also provides a medium for carbon sequestration. It is vital to understand the structural and dynamic impacts of guest variation in CH 4 , CO 2 , and mixed hydrates and link the results to the stability of various deposits in nature, harvesting methane, and sequestering CO 2 . Molecular vibrations are examined in CH 4 , CO 2 , and mixed CH 4 -CO 2 hydrates at 5 and 190 K and Xe hydrates for comparison. Inelastic neutron scattering (INS) is an ideal spectroscopy technique to observe the dynamic modes in the hydrate structure and enclathrated CH 4 , as it is extremely sensitive to 1 H. The presence of CO 2 in hydrates tightens the lattice. It introduces more active librational modes to the host lattice, while hindering the motion of CH 4 in mixed CH 4 -CO 2 hydrate at 5 K. At 190 K, a large broadening of the CH 4 librational modes indicates disorder in the structure leading to dissociation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Validation of the Community Land Model Version 5 over the Contiguous United States (CONUS) using in situ and remote sensing data sets

The Community Land Model (CLM) is an effective tool to simulate the biophysical and biogeochemical processes and their interactions with the atmosphere. Although CLM Version 5 (CLM5) constitutes various updates in these processes, its performance in simulating energy, water and carbon cycles over the Contiguous United States (CONUS) at scales which land surface changes and hydrometeorological and hydroclimatological applications are more locally relevant is yet to be assessed. In this study, we conducted three simulations at 0.125? during 1979-2018 over the CONUS using different configurations of CLM, namely CLM5-biogeochemistry (CLM5BGC), CLM4.5BGC, and CLM5-satellite phenology (CLM5SP). We validated and compared their simulations against multiple remote-sensed and in-situ datasets. Overall, the parametric and structural updates (e.g., carbon cost for nitrogen uptake, variable soil thickness, dry surface layer) in CLM5 improve its ability in capturing terrestrial biogeochemical dynamics. The low evapotranspiration in CLM5BGC is associated with biases in simulating vegetation phenological characteristics rather than soil water limitations. The mismatch between CLM5BGC-simulated peak leaf area index and reference data can be attributed to CLM5BGC's inability in simulating phenology of trees and grasses. The differences between CLM-simulated irrigation and reference estimates can be attributed to differences between processes represented in models and in reality, and uncertainties in input and validation datasets. Evaluation against observations at small catchments suggest that hydrologic parameters needed to be calibrated to improve simulations of runoff, especially subsurface runoff. Additional efforts are needed to incorporate spatially-distributed plant phenology and physiology parameters and regional-specific agricultural management practices (e.g., planting, harvest).

Cheng, Yanyan↗